44 results about "Linear programming algorithm" patented technology

The invention discloses a synergistic proportioning method for multiple scrap aluminum capable of reducing the proportion of pure aluminum ingots, and relates to the technical field of aluminum smelting and secondary aluminum, the method comprises the following steps: S1, collecting and sorting scrap aluminum: collecting any three scrap aluminum from door and window scrap aluminum, automobile scrap aluminum, zip-top can scrap aluminum and industrial leftover scrap aluminum, collecting, and removing impurities in the scrap aluminum; s2, component detection: respectively detecting the aluminum content and impurity elements of each scrap aluminum in the step S1 by using a direct-reading spectrometer; according to the method, the proportion is automatically calculated through a linear programming algorithm, traditional empirical proportion is replaced, the proportion calculation time is shortened, and the batching efficiency is greatly improved. Meanwhile, the qualified rate of finished product detection is improved, the number of reworking times is reduced, the production cycle is shortened, the production efficiency and market response capability of enterprises are improved, and the enterprises are helped to quickly meet customer order requirements.

The invention relates to an intelligent road maintenance closed-loop decision-making system based on budget constraint and fund matching, and belongs to the technical field of road operation and maintenance. The data fusion and recognition layer is used for performing feature extraction, fusion and disease recognition on the multi-source data and outputting standardized disease entries; the decision optimization layer is used for solving an optimal repair strategy by adopting a mixed integer linear programming algorithm based on disease items, material inventory information, budget constraints, construction resources, traffic flow prediction and weather data; the fund matching and work order generation layer is used for outputting financing labels and electronic work orders according to an evaluation and rule engine; and the acceptance and backtracking layer is used for collecting a detection result after construction is completed, calculating a repair quality coefficient and feeding back the repair quality coefficient to the model for updating. According to the invention, full-automatic closed-loop management is realized, the whole process from disease identification to work order issuing to acceptance feedback is free of human intervention, the fund use efficiency is improved, and the repair quality and transparency are improved.

The invention discloses an intelligent observation station planning method based on a ground laser scanner in a complex forest area environment, and the method comprises the following steps: S1, employing a spatial analysis method based on multi-source geometric constraints, and achieving the separation of a single tree hierarchical structure; s2, evaluating the visual range of the candidate points according to occlusion judgment; s3, performing visibility analysis to select efficient candidate points; and S4, selecting a final frame site based on fusion of heuristic search and integer linear programming. According to the intelligent observation station planning method based on the ground laser scanner in the complex forest area environment, firstly, efficient candidate point identification is carried out by adopting multi-source geometric constraint, single tree and candidate point distribution condition identification, shielding judgment and visibility analysis, and then heuristic search and an integer linear programming algorithm are fused; and the final position of the ground laser scanner stand is obtained. Compared with a traditional algorithm, the optimization algorithm based on the visibility constraint can greatly reduce the number of stand stations and improve the calculation efficiency under the condition that the scanning integrity is guaranteed.

This invention discloses a joint clearing method and system for the electricity spot market that considers the generalized energy storage characteristics of electric vehicle aggregators. Based on the operational characteristics and physical constraints of large-scale electric vehicle access to the electricity spot market, and combined with the theory of generalized energy storage efficiency, an embedded constraint model is constructed, including charging and discharging power boundaries, energy state temporal coupling, and terminal electricity demand. This enables quantitative modeling and unified scheduling of the flexibility resources of electric vehicle aggregators. Data cleaning and parameter normalization methods are used to map the characteristics of dispersed electric vehicle clusters to standardized virtual power plant parameters. State equation constraints are introduced into the traditional DC optimal power flow and unit combination model. Combining mixed-integer linear programming algorithms and dual pricing theory, the optimal coordination strategy between conventional unit output and electric vehicle charging and discharging is solved, and the low-cost clearing and nodal marginal price of the spot market are calculated. The method of this invention can improve the level of renewable energy consumption and the economy and security of grid operation.

The application provides an artificial intelligence-based supplier food ERP whole-chain inventory early warning system, relates to the technical field of food inventory monitoring, and comprises a data acquisition and processing module, an inventory prediction module, an inventory early warning module and an ERP execution module; the data acquisition and processing module is used for acquiring whole-chain inventory data and pre-processing the whole-chain inventory data; the inventory prediction module is used for predicting future inventory; the inventory early warning module is used for presetting inventory early warning rules and early warning future inventory; and the ERP execution module is used for generating a restocking strategy. The application accurately predicts future inventory changes by means of a time series model, a machine learning model and a fusion strategy, so that an enterprise can plan production and procurement in advance; potential inventory risks are discovered in time by means of multi-dimensional early warning rules and a risk assessment matrix, so as to reduce out-of-stock or overstock losses; and the ERP execution module generates an optimal restocking strategy based on a cost model and a linear programming algorithm, and the restocking strategy is dynamically adjusted in combination with a real-time state of a supply chain, so as to reduce the costs of procurement, transportation and warehousing while ensuring supply.

The invention discloses a ship energy optimization method considering energy storage life and flexible navigation. The ship energy optimization method comprises the following steps: step 1, constructing a ship flexible navigation model based on marine environment data and an electronic chart system; establishing an energy storage attenuation model of the battery energy storage system, wherein the model comprises quantitative calculation and linear processing of calendar life attenuation and cycle life attenuation; constructing a photovoltaic output model dynamically associated with the ship position, wherein the model comprises solar irradiation calculation, photovoltaic panel efficiency correction and environmental factor influence evaluation; 2, a flexible navigation model, an energy storage attenuation model and a photovoltaic output model are integrated, an SIES multi-objective optimization model is established, and the model aims at minimizing the total cost of the system; and 3, solving the SIES multi-objective optimization model by adopting a mixed integer linear programming algorithm to obtain an optimal route planning, energy storage configuration and energy scheduling scheme.

The application relates to the technical field of cloud computing and servers, and particularly discloses a computing power and power collaborative scheduling optimization method and system based on multiple levels of requirements, wherein the method comprises the following steps: determining a computing power supply-demand gap according to a computing power demand curve and a renewable energy power generation curve; determining a cost function of each period under each scene according to the computing power supply-demand gap, establishing a computing power scheduling model, a power scheduling model and a power scheduling optimization model based on a storage energy source; dividing the cost into a cost saving part and an energy saving and emission reduction part according to the cost function of each period under each scene; and minimizing the cost of the cost saving part and maximizing the income of the energy saving and emission reduction part through an iterative linear programming algorithm, so as to obtain a computing power allocation result of each period; and according to the computing power allocation result, the computing power supply-demand gap is gradually reduced through iteration until the supply-demand gap is smaller than a predetermined threshold, so that a final computing power and power collaborative scheduling scheme is obtained.

This invention discloses a method, system, and equipment for disaster relief using swarm robots. The method includes: simulating a disaster relief scenario; determining the hard rules that each robot in the swarm must follow based on its actions and states in the simulated scenario; constructing a functional relationship between the adoption rate of social rules and the performance indicators of the swarm robots; constructing a compromise decision model based on the performance indicators, the social rules, and the functional relationship; solving the compromise decision model using an adaptive linear programming algorithm and performing multi-objective visual trade-off analysis to obtain the range of social rule adoption rates that satisfies all the performance indicators; and directing the swarm robots to perform disaster relief based on the range of social rule adoption rates. This invention can minimize casualties and property damage in disaster relief.

The application discloses a building integrated energy system operation strategy and declaration capacity optimization method considering demand response effectiveness requirements. First, the basic information of the building user side and the power grid side is collected; second, a building integrated energy system optimization scheduling model is established; then, an optimization solving problem of the building integrated energy system optimization scheduling model considering the demand response effectiveness requirements is determined; finally, a mixed integer linear programming algorithm is used to solve the optimization solving problem of the building integrated energy system optimization scheduling model, and the optimal declaration capacity value scheme and the operation strategy scheme are obtained. By using the application, the requirements of the power grid on the user information declaration can be met when the building user participates in the invitation type demand response, the declaration capacity value recommendation scheme and the response strategy recommendation scheme are provided for the user; and based on the response effectiveness auditing mechanism formulated by the power grid, the actual response effect of the user is improved, and the target of further reducing the operation cost of the user is achieved.

The invention provides an industrial coal blending optimization method and system based on multi-agent collaboration and a program product, and relates to the technical field of industrial intelligent control and data processing. The method comprises the following steps: acquiring production state data of a current time step, wherein the data contains coal inventory, physicochemical indexes and raw material resource weight coefficients; inputting the data into a mechanism calculation module, and calculating a reference ratio vector by using a linear programming algorithm with the purpose of minimizing a comprehensive resource consumption index; meanwhile, inputting the data into a strategy correction agent, and outputting a correction action vector aiming at a reference ratio and a confidence factor of a current working condition; and carrying out dynamic weighted fusion on the reference ratio and the correction action. A double-layer coupling framework of mechanism guarantee and AI efficiency improvement is adopted, a confidence factor is used as a safety valve, on the premise that production safety is guaranteed, the problems that a traditional coal blending model is poor in non-linear adaptive capacity, lagged in feedback and the like are solved, and global optimization of industrial raw material configuration is achieved.

This invention provides a linear programming method and system for scheduling cleaning operations under multiple constraints. It collects multi-dimensional constraint feature values ​​and basic operational data from cleaning operation scenarios, constructs a linear programming mathematical model integrating multiple constraints, and sets an objective function and a set of constraint equations. A linear programming algorithm is used to optimize the model to obtain an initial scheduling scheme. Based on dynamic adjustment rules for cleaning operations, the initial scheduling scheme is iteratively optimized to generate an optimal cleaning operation scheduling scheme adapted to the actual operation scenario, which is then visualized and output. Simultaneously, the operation execution status is monitored in real time, and the scheduling scheme is dynamically updated. Using this invention, intelligent, precise, and dynamic scheduling of cleaning operations under multiple constraints can be achieved, significantly improving the scheduling efficiency and resource utilization of cleaning operations.

The invention discloses a natural gas pipeline network scheduling method, electronic equipment and a storage medium, relates to the technical field of natural gas pipeline scheduling, and aims to determine a better natural gas pipeline network scheduling scheme. The method comprises the following steps: obtaining a linearized mathematical model; solving the target function based on the flow constraint and the operation flow constraint of the compressor at the supercharging node to obtain an initial optimization result; according to the first search step size, based on the initial optimization result and a penalty sequence linear programming algorithm, solving the linearized mathematical model to obtain a first optimization result; according to a second search step length, based on the first optimization result and a penalty sequence linear programming algorithm, solving the linearized mathematical model to obtain a second optimization result; based on the third search step size, the second optimization result and a penalty sequence linear programming algorithm, solving the linearized mathematical model to obtain a third optimization result; and determining a natural gas pipeline network scheduling scheme based on the third optimization result.

The invention discloses an optimized operation method of an off-grid direct-current micro-grid. The off-grid direct-current micro-grid comprises a photovoltaic power generation system, a battery energy storage system, a fuel cell and an electrolytic cell which are connected through a direct-current bus. The optimized operation method comprises the following steps: constructing a system mathematical model of an off-grid direct current micro-grid, and defining a power balance relationship between a direct current load and each energy component; the system mathematical model comprises mathematical models of a photovoltaic power generation system, a battery energy storage system, a fuel cell, an electrolytic cell and a direct current load; based on the system mathematical model, establishing a linear optimization model by taking minimization of hydrogen fuel consumption or maximization of electrolytic cell hydrogen energy production as a target; the constraint condition of the linear optimization model comprises a power balance relation; and solving the linear optimization model by adopting a linear programming algorithm so as to dynamically distribute power output of each energy component, and controlling the operation of the micro-grid based on a solving result. The method can solve the problems that in the prior art, an off-grid direct-current micro-grid is high in operation cost, and an optimization algorithm is complex in calculation and difficult to apply in real time.

The invention discloses an organic waste resource operation optimization management system for pollution reduction and carbon reduction, and relates to the technical field of organic waste treatment, and the system comprises a formula module which carries out the raw material characteristic analysis of organic wastes, and outputs a waste feeding formula; the monitoring module is used for carrying out formula collaborative conversion and state monitoring treatment on the organic wastes to obtain material state monitoring data; the adjusting module is used for carrying out real-time comparison and deviation trend analysis and outputting a dynamic adjusting instruction; the scheme module is used for executing the dynamic adjustment instruction, performing multi-path collaborative scheduling optimization on the material biotransformation data and the energy market information data through a linear programming algorithm, and outputting a multi-path collaborative scheduling scheme; and the execution module is used for quantitatively evaluating the carbon emission reduction benefit of the multi-path cooperative scheduling scheme and outputting a real-time resource allocation instruction and a linkage production plan. According to the invention, through dual-mechanism cooperation of multi-path cooperative scheduling optimization and carbon-energy dynamic tradeoff optimization, pollution reduction and carbon reduction synergistic interaction is realized.

The invention discloses a parameter optimization control system and method for a multistage threshing unit. The core of the system comprises a threshing device / air separator driver, a leaf quality detection module, a data acquisition module, a tobacco leaf beating resistance characteristic knowledge base, an air separator working parameter weight model and a working parameter optimization processing module. By constructing a multi-dimensional matrix of tobacco leaf raw materials and working conditions to map the optimal rolling rotating speed and combining a linear programming algorithm to divide a wind separator task target, parameter initialization recommendation is achieved; and the large-medium leaf rate, the stem content in leaves and the leaf yield of each air separation opening are detected in real time, and the working parameters of the unit are dynamically optimized. According to the scheme, the problems of dependence on manual experience and large quality fluctuation in the prior art are solved, reasonable division of process index tasks of all subsystems of the threshing unit and equipment parameter optimization and automatic control are achieved, the unit can stably operate in the optimal state without manual intervention, the sheet yield and the processing quality are improved, raw material and energy waste is reduced, and the production cost is reduced. The economic benefits of enterprises are improved.

The application discloses a UAV cluster energy efficiency optimization control method and system based on input-output analysis, and belongs to the technical field of UAV energy management. The method collects energy consumption and task execution data of each UAV in real time, constructs a dynamic input-output table, and depicts the correlation between energy flow and task output. The neural network model is used to dynamically identify energy coupling strength coefficients and task contribution weights. These parameters are used as optimization basis, and the linear programming algorithm is used to generate optimal energy distribution instruction set with the minimum unit task energy consumption as the target. The UAV state is adjusted in real time through the instruction distribution and closed-loop optimization mechanism. The system includes data acquisition, dynamic table construction, parameter identification, instruction generation and distribution modules. The application can realize global energy efficiency optimization of the UAV cluster in a complex task environment, and improve energy utilization efficiency and task execution capability.

The invention provides a vehicle accelerator braking control method and device based on mixed integer programming, and relates to the technical field of vehicle intelligent driving. The vehicle accelerator braking control method based on mixed integer programming comprises the steps that according to driving state data of a target vehicle at the current moment and dynamic road information data and traffic situation sensing data of a preset road section in front of the target vehicle, a non-linear programming algorithm is utilized, obtaining a reference speed curve when the target vehicle runs on the preset road section; constructing an objective function and constraint conditions based on the reference speed curve; and based on the constraint condition, solving the target function to obtain the vehicle motion state parameters of the target vehicle at the plurality of look-ahead moments in the driving process of the target vehicle on the preset road section, so as to indicate the target vehicle to drive on the preset road section based on the vehicle motion state parameters of the plurality of look-ahead moments. According to the invention, intelligent control of the accelerator / brake of the vehicle can be safely and comfortably carried out with lower energy consumption.

The present application belongs to the technical field of flexible resource scheduling management, and discloses a virtual power plant multi-element flexible resource daily rolling optimization scheduling method, system, equipment and medium considering random deviation, to solve the problems of poor coordination between resources and high cost of resource regulation. The method comprises the following steps: obtaining resource configuration parameter information, a preset scheduling plan for flexible resources and corresponding response daily operation boundary information; establishing a control model for each type of flexible resource and setting a constraint condition; obtaining the quantity-price curve and adjustable interval of the flexible resource, and constructing a target function based on the same, with the minimum power tracking cost and the minimum power tracking deviation of the response scheduling plan as the target; based on the target function, a mixed integer linear programming algorithm is used to solve each control model, the corresponding power deviation correction value is obtained, and each flexible resource is scheduled based on the same; based on the operation result after scheduling at the current time, the above steps are re-executed at the next time.

The invention discloses a heterogeneous energy storage cooperative climbing auxiliary service and electric energy combined clearing optimization method and system, and belongs to the field of electric power system operation and electric power markets. Aiming at the defects of homogeneous modeling, extensive climbing evaluation and the like in the prior art, an energy power station and a matched energy storage aggregation joint unit are innovated for the first time, a three-layer hierarchical collaborative response logic is constructed with a thermal power generating unit and independent energy storage, and a climbing demand is accurately matched according to differential response delay and function positioning. And constructing an integrated joint clearing model containing the system-level constraint and the main body exclusive refined climbing constraint, taking the minimum total cost of the whole period of the system as a target, solving by adopting a mixed integer linear programming algorithm, and outputting the clearing electric quantity, the climbing capacity and the price of each main body. According to the method, an independent climbing service market mechanism is set, the climbing capacity value is dominated, heterogeneous energy storage accurate cooperative compensation is achieved, the rapid climbing requirement of a high-proportion new energy system can be economically met, and the system operation safety and the new energy consumption level are improved.

The invention discloses a large-scale academic conference intelligent scheduling system and method based on multi-algorithm fusion, belongs to the technical field of conference management, and aims to solve the problems of low resource allocation efficiency, scattered themes, author time conflict and inconvenient user query in existing large-scale academic conference scheduling. The system comprises a data acquisition module, a theme clustering module, a scheduling optimization module and a front-end display module. According to the method, conference paper data are obtained through a web crawler, text features are extracted through a TF-IDF algorithm, paper theme classification is achieved in combination with K-means clustering, then an optimized scheduling scheme is generated through a greedy algorithm or an integer linear programming (ILP) algorithm, and finally a date / conference room screening query function is provided through a front-end interface. According to the method, the conference room use cost can be reduced, the greedy algorithm is suitable for real-time large-scale scheduling, the ILP ensures the constraint satisfaction degree, and the conference scheduling efficiency and the conference participation experience are remarkably improved.

The application discloses a kind of container floor production resource scheduling methods, it is related to container manufacturing technical field, including S1, through sensor array acquisition floor processing process heat conductivity coefficient data and local temperature gradient distribution data, using fourier transform method processing data extraction low frequency domain characteristics, obtain the trend of space-time uneven change of heat conductivity coefficient;S2, according to the trend of space-time uneven change, judge whether the current processing state deviates from the preset heat conduction performance threshold, if deviates then through constraint condition setting and objective function definition start linear programming algorithm, optimize material addition proportion and temperature control parameter, determine adjusted parameter combination;The container floor production resource scheduling method, significantly improve the stability of heat conduction performance, reduce resource consumption, realize the efficient and accurate control of microscale processing.

The invention discloses a method for solving a feedback set of a network, which relates to the technical field of software engineering, logic circuit design and social networking services, and comprises the following steps of: for a graph G, searching a cut edge of the graph G by using a depth-first search algorithm, and if the graph G has the cut edge, deleting the cut edge; if the maximum degree of the graph G is 0, ending the process, and outputting a feedback set; if the B is an isolated point, deleting vertexes in the B, and if the maximum degree of the B is at least 2, firstly putting a vertex v with the maximum degree in the B into a feedback set; according to the method, the feedback set of the network is directly obtained by alternately deleting the vertex with the maximum degree of the network and deleting the cut edge by utilizing the structural characteristics of the graph, and compared with the traditional solving modes such as a linear programming algorithm and a polyhedron combination algorithm, the solving method does not need to construct an adjacent vector and a feedback set polyhedron firstly, so that the solving efficiency is improved. The problem is converted into a linear programming problem, a feedback set result is directly obtained, solving is simpler and more convenient, and the result is more visual.

The invention relates to the technical field of nutrition state evaluation, in particular to an evaluation system and method for a nutrition state, and the method comprises the steps: obtaining the basic information and health data of a patient, and matching a suitable nutrition screening scale; a nutrition screening result is generated through weighted score calculation; performing diet review survey for preset continuous time on the patient with the nutrition risk, calculating nutrient intake level, and generating diet survey data; collecting a biochemical detection result and body measurement data of the patient, combining dietary survey data and basic information, performing comprehensive evaluation through a multi-dimensional evaluation scale, and generating a nutrition evaluation result; the basal metabolic rate is calculated, the nutrition proportion is optimized through a linear programming algorithm, and a nutrition prescription is formulated; integrating the nutrition screening result, the dietary survey data, the body measurement data, the biochemical detection result, the nutrition evaluation result and the nutrition prescription to generate a nutrition evaluation report. According to the invention, individualized and refined nutrition intervention is realized.

The application discloses a kind of substation load control method and system, it is related to the technical field of substation control Step S1, initial data set is obtained by acquisition, the initial data set includes real-time data set and substation parameter set;Linear DistFlow power flow equation set is established, and initial data set is substituted to carry out equation set solving calculation and obtain open-loop state data set;Unlooped stage optimization model is constructed, and undetermined decision variable is solved by mixed integer linear programming algorithm, and optimal decision variable is obtained, and unlooped stage optimal control instruction set is generated;Optimal decision variable is substituted into linear DistFlow power flow equation set, and secondary equation set solving calculation is carried out to obtain looped state prediction data set;Based on the overcurrent protection setting value in initial data set, looped safety check is carried out, and looped safety check result is obtained.The present application can efficiently reduce the impact of looped operation on distribution network.

The invention belongs to the technical field of computers, and particularly relates to a steel trade order signing task automatic generation and field assistance scheduling method. The method aims at solving the problems that a traditional order signing process depends on manpower, the scheduling efficiency is low, and the strain capacity is weak. Customer order data in an enterprise resource planning system are acquired in real time, multi-dimensional feasibility evaluation is performed in combination with customer credit, inventory state and logistics capability, and a structured order signing task work order is automatically judged and generated; an outworker dynamic resource pool containing four-dimensional attributes of position, load, skill and efficiency is constructed, an improved mixed integer linear programming algorithm is adopted to realize multi-target optimization scheduling, and geographic proximity is considered. The method also integrates thermodynamic diagram visualization and load prediction functions, and supports pre-deployment and manual intervention. By means of the scheme, the order signing automation rate exceeds 97%, the order signing period is compressed to be within 8 hours, the overall response time is shortened by 42%, and the abnormal overstock rate is reduced to be below 5%.

The invention belongs to the technical field of linear programming algorithm hardware acceleration, and discloses a hardware acceleration method and device for a PDLP linear programming algorithm, and the method achieves the deep fusion of operators through matrix multiplexing and computational graph reconstruction, enables the SpMV operation and vector operation to be fused into two times of operator calling, and remarkably reduces the access of an off-chip memory. The device comprises a control unit, a read-write unit, a data memory and a multi-data stream calculation unit, and supports CSR / CSC double-format SpMV operation and various vector operations. A reconfigurable PE unit based on floating point FMA is adopted, and the calculation precision and energy efficiency are improved. According to the method, the memory access bottleneck problem of the PDLP algorithm is effectively solved, and the solving efficiency and the energy efficiency ratio of the large-scale linear programming problem are greatly improved.

This invention discloses a production management method and system for single-phase prepaid smart meters, relating to the field of intelligent manufacturing. The method includes: collecting customer order data, inventory data, and supplier data; setting priority thresholds to determine the priority of pre-processed customer order numbers, defining nodes, edges, and attributes, and generating a production graph; constructing a dynamic production optimization model to obtain priority production batches through production batch optimization; allocating priority production batches using a linear programming algorithm to obtain production tasks for these batches, and analyzing these tasks to obtain an equipment requirement list; verifying the raw material requirements in the production tasks using material requirements planning (MRP) technology to obtain raw material warehousing information; and using a blockchain platform to compare the equipment requirement list and raw material warehousing information to generate a production completion report. This invention, by constructing a dynamic production optimization model, achieves intelligent sorting and optimization of production batches for single-phase prepaid smart meters.

The invention relates to a time-space-spectrum three-domain collaborative bandwidth prediction method based on a high-dynamic airborne network. The method comprises the following steps: acquiring airborne multi-dimensional original data through a node of a preset airborne network; processing the multi-dimensional original data to obtain three-domain feature data; based on the three-domain feature data, constructing a time-space-spectrum three-domain collaborative prediction model, and performing three-domain prediction; constructing a judgment matrix through an analytic hierarchy process based on the three-domain prediction result, calculating a weight vector of a corresponding domain, performing consistency check, obtaining a weight distribution result, and performing weighted summation to generate a bandwidth prediction value; obtaining a bandwidth allocation scheme through a linear programming algorithm, and updating a flow table based on an SDN controller to obtain differentiated bandwidth resource allocation; and the deviation between the actual bandwidth of the network and the predicted value is monitored in real time, and the bandwidth is adjusted in real time by using a dynamic adjustment mechanism.

The invention belongs to the technical field of waste power battery recovery, and discloses a method for detecting, sorting, recombining and integrating waste power batteries. The method comprises the following steps: acquiring state parameters of a waste power battery, and evaluating charge and health states through an evaluation model integrating model driving and data driving; based on an evaluation result, carrying out adaptive clustering preliminary sorting and feature extraction secondary grouping to obtain a consistency matching battery pack; in combination with the electrical parameter requirements of the optical storage charging station, a linear programming algorithm is adopted to determine series-parallel configuration and recombine; the active equalization battery management system monitors the operation state of the reconstitution battery system, and adjusts charging and discharging parameters in combination with scene working condition fluctuation. According to the invention, the consistency and the operation stability of the recombined waste power battery are improved, and the application requirements of a light storage charging station are met.